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Abstract
Alzheimer’s disease and other types of dementia are the top cause for disabilities in later life and various types of experiments have been performed to understand the underlying mechanisms of the disease with the aim of coming up with potential drug targets. These experiments have been carried out by scientists working in different domains such as proteomics, molecular biology, clinical diagnostics and genomics. The results of such experiments are stored in the databases designed for collecting data of similar types. However, in order to get a systematic view of the disease from these independent but complementary data sets, it is necessary to combine them. In this study we describe a heterogeneous network-based data set for Alzheimer’s disease (HENA). Additionally, we demonstrate the application of state-of-the-art graph convolutional networks, i.e. deep learning methods for the analysis of such large heterogeneous biological data sets. We expect HENA to allow scientists to explore and analyze their own results in the broader context of Alzheimer’s disease research.
Highlights
Background & SummaryAlzheimer’s disease (AD) is an age-related neurodegenerative disorder that progresses with age and eventually leads to death
In this study we describe a heterogeneous network-based data set for Alzheimer’s disease (HENA)
The results obtained from such experiments are collected in various databases that were created for depositing and providing further access to similar types of experimental biological data such as ArrayExpress, IntAct, hu.MAP, and ADNI7–10
Summary
Alzheimer’s disease (AD) is an age-related neurodegenerative disorder that progresses with age and eventually leads to death. Protein-protein interactions (PPI), gene expression, and medical imaging, have uncovered substantial information about disease mechanisms The majority of such studies have focused on analysis of a particular experimental data type, resulting in the identification of several markers and biological interactions associated with Alzheimer’s disease[2,11,14,15,16]. HENA combines 64 distinct computational and experimental data sets of six data types originating from nine data sources, as described in Online-only Table 1 and Fig. 2 These data types include protein-protein interactions, gene co-expression, epistasis, genome-wide association studies (GWAS), gene expression in different brain regions, and positive selection data. We collected data sets containing Alzheimer’s disease-related information about SNPs, genes and proteins These data sets were combined to constitute a table of node attributes. In the description of a use case, we demonstrate how combined heterogeneous data sets in the network format can help to develop a better understanding of Alzheimer’s disease
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